Genome-wide association analyses of chronotype in 697,828 individuals provides new insights into circadian rhythms in humans and links to disease

Jones, Samuel E.; Lane, Jacqueline M.; Wood, Andrew R.; Hees, Vincent T. van; Tyrrell, Jessica; Beaumont, Robin N; Jeffries, Aaron R.; Dashti, Hassan S.; Hillsdon, Melvyn; Ruth, Katherine S.; Tuke, Marcus A.; Yagohootkar, Hanieh; Sharp, Seth A.; Ji, Yingjie; Harrison, James W.; Dawes, Amy; Byrne, Edna M; Tiemeier, Henning; Allebrandt, Karla V.; Bowden, Jack; Ray, David; Freathy, Rachel M.; Mazzotti, Diego R.; Gehrman, Philip; Lawlor, Deborah A; Frayling, Timothy M.; Rutter, Martin K.; Hinds, David A.; Saxena, Richa; Weedon, Michael N.

doi:10.1101/303941

Cited by 17 publications

(32 citation statements)

References 97 publications

(111 reference statements)

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“…The analysis presented in this paper will facilitate feasible large-scale population research on sleep and physical activity. In addition to the proof of validity as provided in this paper additional support for the credibility of the algorithm was found in our separate study (non-peer reviewed preprint on bioRxiv) identifying genome wide associations with sleep parameters derived from our algorithm in UK Biobank, replicating signals previously associated with self-reported sleep duration and chronotype [27][28][29][30][31][32][33][34] . Our algorithm can be applied to data from the three most widely used accelerometer brands: Actigraph, Axivity, and GENEActiv, and is available as part of open source R package GGIR (https://cran.rproject.org/web/packages/GGIR/).…”

Section: Discussionsupporting

confidence: 75%

Estimating sleep parameters using an accelerometer without sleep diary

Hees

Sabia²,

Jones

et al. 2018

Preprint

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Wrist worn raw-data accelerometers are used increasingly in large scale population research.We examined whether sleep parameters can be estimated from these data in the absence of sleep diaries. Our heuristic algorithm uses the variance in estimated z-axis angle and makes basic assumptions about sleep interruptions. Detected sleep period time window (SPTwindow), was compared against sleep diary in 3752 participants (range=60-82years) and polysomnography in sleep clinic patients (N=28) and in healthy good sleepers (N=22). The SPT-window derived from the algorithm was 10.9 and 2.9 minutes longer compared with sleep diary in men and women, respectively. Mean C-statistic to detect the SPT-window compared to polysomnography was 0.86 and 0.83 in clinic-based and healthy sleepers, respectively. We demonstrated the accuracy of our algorithm to detect the SPT-window. The value of this algorithm lies in studies such as UK Biobank where a sleep diary was not used.

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Section: Discussionsupporting

confidence: 75%

Estimating sleep parameters using an accelerometer without sleep diary

Hees

Sabia²,

Jones

et al. 2018

Preprint

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“…A nonsynonymous variant in LRRK2 that causes Parkinson's disease 44 was associated with a 5-fold odds of having a parent with Parkinson's disease. A nonsynonymous variant in PER3 previously classified as pathogenic for advanced sleep phase syndrome had an odds ratio of only 1.38 for being a morning person 45,46 compared to a reported 2 hour shift in midpoint sleep. Height, skeletal weight and male pattern baldness were negatively associated with two nonsynonymous variants in AR that cause partial androgen insensitivity syndrome 47 .…”

Section: Reduced Penetrance Estimates For Known Pathogenic Variantsmentioning

confidence: 90%

Assessing the pathogenicity, penetrance and expressivity of putative disease-causing variants in a population setting

Wright

West

Tuke

et al. 2018

Preprint

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Over 100,000 genetic variants are classified as disease-causing in public databases. However, the true penetrance of many of these rare alleles is uncertain and may be over-estimated by clinical ascertainment. As more people undergo genome sequencing there is an increasing need to assess the true penetrance of alleles. Until recently, this was not possible in a population-based setting. Here, we use data from 388,714 UK Biobank (UKB) participants of European ancestry to assess the pathogenicity and penetrance of putatively clinically important rare variants.Although rare variants are harder to genotype accurately than common variants, we were able to classify 1,244 of 4,585 (27%) putatively clinically relevant rare variants genotyped on the UKB microarray as high-quality. We defined "rare" as variants with a minor allele frequency of <0.01, and "clinically relevant" as variants that were either classified as pathogenic/likely pathogenic in ClinVar or are in genes known to cause two specific monogenic diseases in which we have some expertise: Maturity-Onset Diabetes of the Young (MODY) and severe developmental disorders (DD). We assessed the penetrance and pathogenicity of these high-quality variants by testing their association with 401 clinically-relevant traits available in UKB.We identified 27 putatively clinically relevant rare variants associated with a UKB trait but that exhibited reduced penetrance or variable expressivity compared with their associated disease. For example, the P415A PER3 variant that has been reported to cause familial advanced sleep phase syndrome is present at 0.5% frequency in the population and associated with an odds ratio of 1.38 for being a morning person (P=2x10 -18 ). We also observed novel associations with relevant traits for heterozygous carriers of some rare recessive conditions, e.g. heterozygous carriers of the R799W ERCC4 variant that causes Xeroderma pigmentosum were more susceptible to sunburn (one extra sunburn episode reported, P=2x10 -8 ). Within our two disease subsets, we were able to refine the penetrance estimate for the R114W HNF4A variant in diabetes (only ~10% by age 40yrs) and refute the previous disease-association of RNF135 in developmental disorders.In conclusion, this study shows that very large population-based studies will help refine the penetrance estimates of rare variants. This information will be important for anyone receiving information about their health based on putatively pathogenic variants.

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“…Genome-wide association studies (GWAS) were previously performed for seven self-reported measures of habitual sleep patterns including chronotype[28], sleep duration[29], long sleep duration[29], short sleep duration[29], frequent insomnia[30], excessive daytime sleepiness and daytime napping. GWAS have also been performed for three accelerometer-measured measures of sleep including timing of the least active 5 hours of the day (L5 timing), nocturnal sleep duration and sleep fragmentation[31].…”

Section: Methodsmentioning

confidence: 99%

Is disrupted sleep a risk factor for Alzheimer’s disease? Evidence from a two-sample Mendelian randomization analysis

Anderson

Richmond

Jones

et al. 2019

Preprint

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INTRODUCTIONIt is established that Alzheimer’s disease (AD) patients experience sleep disruption. However, it remains unknown whether disruption in the quantity, quality or timing of sleep is a risk factor for the onset of AD.METHODSMendelian randomization (MR) was used to estimate the causal effect of self-reported and accelerometer-measured sleep parameters (chronotype, duration, fragmentation, insomnia, daytime napping and daytime sleepiness) on AD risk.RESULTSOverall, there was little evidence that sleep traits affect the risk of AD. There was some evidence to suggest that self-reported daytime napping was associated with lower AD risk (odds ratio [OR]: 0.70, 95% confidence interval [CI]: 0.50 to 0.99). Some other sleep traits (accelerometer-measured eveningness and sleep duration, and self-reported daytime sleepiness) had ORs for AD risk of a similar magnitude to daytime napping, but were less precisely estimated.DISCUSSONOur findings provide tentative evidence that daytime napping may reduce AD risk. However, findings should be replicated using independent samples.

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Genome-wide association analyses of chronotype in 697,828 individuals provides new insights into circadian rhythms in humans and links to disease

Cited by 17 publications

References 97 publications

Estimating sleep parameters using an accelerometer without sleep diary

Estimating sleep parameters using an accelerometer without sleep diary

Assessing the pathogenicity, penetrance and expressivity of putative disease-causing variants in a population setting

Is disrupted sleep a risk factor for Alzheimer’s disease? Evidence from a two-sample Mendelian randomization analysis

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